Mustansiriyah journal of pure and applied sciences

the theoretical results were than compared
with the results predicted by using the programs (xcom, ffast) noticeable differences were
observed in the values of the theoretical results and the values of the experimental or semiexperimental results from programs (Xcom, Ffast) because the theoretical equation considers
the material to be homogeneous and ideal and depends on the absorption resulting from the
photoelectric effect, this is in addition to the effect of absorption edges. The equation
theoretical followed the same experimentally proven course: with increasing X-ray energy, the
mass absorption coefficient of material decreases,.All calculations and graphical
representations were performed using MATLAB 2020. The linear correlation coefficient
between the theoretical and experimental results was also calculated using one of advanced
MATLAB, algorithms were used to demonstrate the degree of agreement between the results
MATLAB 2020 theoretical and experimental statistical method using function Cftool.

In this paper, we suggested a new four-term CG method, by expanding the three-term CG
method to the four-term CG method. The search direction p, with some newly proposed
parameters, is a four-term hybrid form for modified traditional CG methods. This hybridization
yielded a newly created hybrid CG method with four terms as its ultimate result. The new
method proposed ( the four-term CG method ) fulfills the conjugacy condition by the Dai-Laiao
method and possesses sufficient descent property. Also, it was confirmed that the suggested
new method would globally converge to the general Wolfe conditions beneath an adequate
assumption. Finally, a comparison was made between the three terms CG that were adopted in
the expansion and the proposed four-term formula, the computational results demonstrated the
new method's robustness and effectiveness.

Atherosclerosis is a chronic condition where plaques composed of lipids build up in
arterial walls, causing them to harden and narrow. Oxidized low-density lipoprotein (OxLDL)
plays a crucial role in this process by triggering inflammatory responses and contributing to
plaque formation. Nitric oxide (NO) is essential for maintaining vascular health, but its
bioavailability is often reduced in atherosclerosis, exacerbating the condition. Objective:
Evaluation of levels of oxLDL as an indicator of oxidative stress and assessment its effect on
nitric oxide levels. Methods: This study included determination of the levels of oxLDL and
nitric oxide in sera samples from 100 atherosclerosis patients in comparison with age and sex
matched healthy donors. Results: The results showed that there is a statistically significant
increase (p<0.01) in oxLDL levels in atherosclerotic individuals as compared to control group,
while a statistically significant reduction (p<0.01) was observed in nitric oxide levels in
patients’ group. Conclusion: The data indicates that elevated levels of oxidized low-density
lipoprotein (OxLDL) and reduced levels of nitric oxide (NO) are significant markers in the
pathogenesis of atherosclerosis. Oxidative stress acts as a critical trigger for the formation of
OxLDL, which in turn amplifies pro-inflammatory responses within the vascular system. High
OxLDL levels contribute to endothelial dysfunction and promote inflammation, exacerbating
plaque formation. Concurrently, decreased NO levels impair vascular relaxation and increase
oxidative stress, further aggravating the condition. This interplay between oxidative stress, high
OxLDL, and decreased NO highlights the complex mechanisms driving atherosclerosis and
underscores the potential for therapeutic interventions targeting these pathways.

Restoration is the process of returning a degraded image to its original state. Image restoration
is a significant problem in high-level image processing that deals with restoring an original and
sharp image using a degradation and restoration model. Eliminating blur and noise from images
is a difficult problem to solve, and it is more difficult for researchers to find an effective way to
remove the noise from the images.
Sometimes, the captured image gets blurry and there is noise from the environment,
Degradation may occur due to noise, motion blur, Gaussian blur, camera mismatch, non
uniform illumination, and low-contrast image. Depending on the pupil function technique for
images of point objects, the proposed method integrates the design of a new amplitude filter
mask to improve image quality. our filter was intended to restore blurry and noisy images using
MATLAB software, The performance of this algorithm has been compared with CLAHE and
HE algorithms. the statistical image quality measurement is based on the signal-to-noise ratio
(PSNR), Error Relative Global Dimensionally de Synthase (ERGAS), image entropy,
Structural Similarity Index (SSIM), Root Mean Square (RMSE), and Enhancement Measure
Estimation (EME). The results showed that the proposed method succeeded in improving
degraded images and the experiment results show the quality measures give a good result for
all type of degraded images . the PSNR gives good result for low light images .while the values
of ERGAS gives good result for all type of degraded images . SSIM, and entropy performs
much better than PSNR and EME

The development and advancement of technology and the transfer and sending of images over
the Internet, protecting images from brute-force attacks has become necessary images from
brute-force attacks. Therefore, one crucial step in securing images from intrusion or unwanted
access is image encryption. In this paper, the E0 algorithm is used in Bluetooth encryption, but
here we will use the keystream generator of this algorithm in image encryption once and use
the logistic map and the E0 key stream generator again, and then we will compare the results of
these methods. The new methods take advantage of the disordered movement of logistic maps
to further improve the confusion and diffusion aspects of image encryption processes. The E0
algorithm can produce a stream that appears to be random, which is then used for image
encryption, which leads to higher entropy and lower correlation with the image. Several
different tests ware done to evaluate the performance of the encoder scheme generated using
E0 algorithm and logistic map, including randomness tests, correlation coefficient analysis
parameters, uniform average intensity of change (UACI) , number of pixels change rate
(NPCR), and other tests that prove its efficiency. The paper concludes that the E0 algorithm
enhanced by the logistic map not only provides more secure and efficient image encryption but
also focuses on preserving the confidential information in the image more effectively.

In this paper, we define the notion of new class of soft sets, called soft strongly Ƅ∗ −closed and
soft strongly Ƅ∗ −open sets. We introduce the soft strongly Ƅ∗ −continuous functions and study
several properties of images and inverse images and define open map and closed map. In
addition, we introduce a several characterizations and properties and for better understanding
examples have been addressed.

The main aim of this study was to develop a straightforward procedure for synthesis
magnesium oxide nanoparticles (MgO NPs) that used as antimicrobial agent against Serratia
marcescens, a gram negative bacterium which isolated from post-operative wound infection.
A high -purity nanoparticles of MgO has been prepared by applying deferent calcinations
temperature (200ºC, 400ºC, 650ºC) using hydrothermal chemical method. The hydrothermal
technique is a process used to create a crystalline nanomaterial’s from high-temperature
aqueous solutions with high vapour pressures, this way can be prepared more shape of
nanoparticles that are highly stable. The structural, morphological and optical properties of
MgO NPs were conducted using : XRD, FE-SEM and UV-vis analysis respectively. The
crystallinity it increases with an increase of calcination temperature that reducing the effect
on antibacterial activity were done on pathogenic bacterium as result of increase in particle
size with sharp intense peak. The antimicrobial effects of the prepared MgO NPs were
examined against multi-drag resistance (MDR) strains using Micro-dilution titer method with
various doses of minimum inhibitory concentration (MIC) of MgO NPs (1500, 1000, 500,
250, 125, 62.5, 31.5 µg/ml-1 ). The higher concentration value which decreased the bacterial
growth was recorded at 1500µg/ml-1 at 650 ºC, and the lower was recorded as 31.5 µg/ml-1 at
200ºC. This preparation procedure yielded higher nanoparticles effectiveness in fast, simple
and economical manner that have active role against microbial growth.

intraocular pressure that can damage the eye by destroying the nerve cells in the retina in
addition to the optic nerve of the eye. The need for detecting and recognizing glaucoma eye
disease increased worldwide. The current paper presents a novel Convolutional Neural
Network based on an intelligent pattern categorization method upon ACRIMA dataset images.
The proposed technique for detecting and classifying the glaucoma in fundus images consisted
of a multi-layer deep neural network model with convolutional and classification layers. The
main contribution of the current study focuses on the proposed model’s achievement in
classifying funds images, verify the impact of the number of filters upon two values of Epoch
(50 and 100) and analysis the variation of confusion matrices with increasing the number of
filters through the two mentioned Epoch values. The role of the dropout layer affects upon
classification procedure and hence a robust CNN model resulted such that highest values of TP
and TN lie in the range (0.97-0.99) and (0.99-1) respectively for all used filters, otherwise Fp
and FN recorded the lowest values and hence the proposed model at final succeeded with
significant advancement in comparison with other related studies in classifying glaucoma
disease.

The objective of this research is to study the Seebeck effect on certain polymers as well as the
effect of temperature on their energy gap, therefore the research is divided into two parts, the
first concerning the Seebeck effect, and its samples were prepared by adding different
concentrations (0.0, 0.028, 0.054, 0.079%) of sodium chloride, sodium carbonate and
potassium sulfate to solutions of each of PVA, PEG, CMCHV, PVP and PAM in twice-distilled
water. The Seebeck effect coefficient remained practically unchanged with the addition of
different concentrations of additives in PEG solutions, but increased with increasing
concentrations and temperature in PVP solutions. As for CMCHV solutions and PAM
solutions, it decreased with increasing concentrations and temperature .The second part
measures the change of energy gap of thin polymer films to which different concentrations of
citric acid have been added (40, 57, 66, 72%) due to the change in temperature. The energy gap
decreased for all samples with increasing temperature and citric acid concentrations. These
results will allow to improve the thermoelectric and electrical properties of polymers and use
them in many fields such as industry, medicine and science.

This work presents the production of pure zinc oxide (ZnO) films and carbon nanoparticle
(CNP) decorated ZnO (CNP/ZnO) nanostructured films for glucose sensors, this study
describes a potentially useful method. Carbon nanoparticles were created via the hydrothermal
process, and zinc oxide nanoparticles with an average particle size of 11.14 nm were grown on
zinc foil using the anodization method. Using the ultrasonication method, carbon nanoparticles
were decorated on ZnO film nanocomposites. Numerous experiments were conducted, such as
the examination of the microstructure, the analysis of crystallinity, and the study of
electrochemical properties. Photoluminescence (PL) measurements, energy-dispersive X-ray
spectroscopy (EDX), field-emission scanning electron microscopy (FE-SEM), transmission
electron microscopy (TEM), UV-vis spectroscopy, and X-ray diffraction (XRD) were the
methods used to determine the nanostructures When the CNPs/ZnO nanostructures were
compared to pure zinc oxide nanostructures, the electrochemical analysis of the former greatly
improved the sensitivity to glucose. Using a linear range of 0.3 mM to 1 mM and a low
detection limit of 1.15 mM, the CNP/ZnO nanostructures improved glucose sensing with a
sensitivity of 2372 µA.mM-1cm-2.

The aim of this paper is to use the action of some cyclic subgroups of a projective general
linear group of order eight over the finite field of order two, ????????????28 , to construct complete arcs
of degree higher than three in ????2(7). Six arcs are found by group action, and from these arcs
many complete arcs are constructed. As a result of thess actions a lower bound for maximum
size of arcs of certain degree are found in ????2(7).

Everyone needs security, to use the internet, systems, and applications safely,
protect their information and data, and prevent undesirable access. Authentication
plays a critical role in security. There are many types of authentications, each one
produces a unique method to confirm a user’s identity. Password-based
authentication is the most popular method, despite vulnerabilities and breaches. In
this paper, a brief overview of traditional user authentication methods (Passwords,
Smart-Card Authentication, and Digital Certificate Authentication) and Biometric
Methods are provided. In addition, the Strengths and the Vulnerabilities of each
technique are described. Biometric authentication is based on a unique
physiological or behavioral character like the user's fingerprint, voice, retinas, and
facial features. It provides powerful security depending on unique characteristics
and features that are difficult to replicate or steal. Thus, biometric authentication
provides more reliable user identification than other methods despite that
traditional method like passwords are prevalent, biometric authentication offers
highly reliable performance in security. The basis of the biometric system is a
model recognition system

The bio ceramic constant of zirconium dioxide and calcium oxide with a weight ratio of 1:1. It
was prepared using a ceramic grinder with hardened ceramic balls for 10 continuous hours at a
rotational speed of 350 rpm. The resulting powder was then collected and pressed into a mold
with a pressing capacity of 5 tons, then sintering process was performed at a temperature of
850oC for 4 hours the sintering samples of calcium zirconate subjected to milling for 24 hrs to
obtain a very fine powder on the at nano scale. Structural examinations of Calcium Zirconate
(CaZrO3 )powder were carried out by utilizing such as X-ray diffraction and scanning electron
microscopy SEM, and Raman spectrum. A mixture of powder was prepared in the form of a
paste by adding 30% of polylactic acid PLA for the purpose of making ceramic cement. It can
be stored in ampoules for use in treating cracks in bones. The results of the hardness and
toughness test of bone cement showed that the percentage of the binding material (polymer) is
30%.

In this research, silver oxide nanoparticles were prepared using the sol-gel method, and
measurements were made, namely XRD, FESEM, FTIR and EDX, and the nanoparticles were
confirmed, and then these nanoparticles( Ag2O) were used as a powder against the formation of
the biofilm of bacteria ( bacterial adhesion measurement), where five types of bacteria were
used (E. coli, Klebsiella pneumoniae, Pseudomonas aeroginosa , P. mirabilis and
Stenotrophomonas maltophilia), where these particles showed their effectiveness significantly
on the formation of the biofilm of bacteria, especially bacteria Klebsiella pneumoniae at
0.53O.D as well as Pseudomonas 0.4O.D , while bacteria E. coli, P. mirabili and
Stenotrophomonas maltophilia showed resistance at different concentrations.

Absence issues among employees create numerous problems for workplace productivity and
operational continuity. The paper analyzes machine learning approaches to predicting
employee absenteeism based on controlled workforce information. Scientists applied a
thorough preprocessing pipeline to a realistic dataset, which involved dealing with missing
values as well as encoding categories along with feature numerical scaling. An exploratory
analysis of the data and feature importance evaluation helped identify proper predictive
attributes.
Random Forest, together with XGBoost and Multi-Layer Perceptron (MLP) Neural Network,
served as the classification models to create predictive models, which were assessed using
accuracy and precision and recall, and F1-score metrics. XGBoost proved its superiority as a
predictive model by reaching 81.03% accuracy during testing while showing balanced results.
This study highlights that ensemble-based machine learning approaches present opportunities
to boost data-driven decision-making for human resource management operations. The coming
paper will concentrate on adding more content to datasets as well as improving model visibility
while studying adaptive learning methods to enhance real-world systems and stability.